AIVIS: Next Generation Vigilant Information Seeking Artificial Intelligence-based Clinical Decision Support for Sepsis

摘要 脓毒症是一种异质性综合征，其特征是由身体的炎症引起的全身炎症。 对感染的反应，是医院治疗的最昂贵和最致命的疾病， 仅在美国就有一例败血症相关的死亡病例。最佳脓毒症护理的基石是早期 识别伴随适当的抗菌治疗，并使用循证血流动力学 治疗，如液体复苏和血管活性药物。虽然数据驱动的方法基于 机器学习（ML）已经显示出在高维临床数据中发现模式以进行预测的前景 在住院患者中，没有临床验证和FDA批准的临床决定 这是一种可以可靠地识别有发生败血症风险的患者的CDS支持系统。此外，现有 基于ML的解决方案与提供给它们的数据质量以及离群值的存在一样好 并且缺失会对它们的性能产生有害影响。例如，有人建议， 这些系统本质上是在观察临床医生的肩膀-使用临床行为作为表达 预先存在的直觉和怀疑来产生预测。因此，对败血症的治疗是非常必要的。 预测工具，可以有效地使用常规收集的EHR数据，评估预测的信心， 如有必要，采取必要措施收集更多信息，以减少预测的不确定性， 提高诊断准确性，而对最终用户没有很大的要求。 本项目旨在评估一种新型不确定性意识脓毒症的临床实用性、安全性和有效性 预测系统由加州大学圣地亚哥分校健康和Healcisio合作设计和开发 股份有限公司、加州大学圣地亚哥分校的一家初创公司，专注于先进分析技术的可扩展开发和商业化 重症监护环境中的系统。Healcisio系统明确设计用于改善以下方面的合规性： 医疗补助和医疗保险服务中心（CMS）脓毒症护理方案（SEP 1包）， 解决确定脓毒症发作时间的现有延迟和可变性， 抗生素和血液动力学支持可以及时地输送。维护软件质量 确保将开发质量管理体系（QMS），以随附510（k）FDA申报资料 包装，以证明安全性和有效性。加强医院质量改进（QI）团队 能够衡量早期识别和SEP-1捆绑合规性的影响，这是一种新的质量指标 (SEP1+），并介绍了因果影响分析工具。最终，新技术的发展， 在该项目下进行的测试将增强我们使用高级分析来预测不良事件的能力， 评估患者对治疗的反应，并通过快速循环优化和个性化护理 “学习型医疗体系”框架。

Abstract Sepsis, a heterogeneous syndrome characterized by whole-body inflammation caused by the body's response to an infection, is the most expensive and deadly condition treated in hospitals, with over 270,000 cases of sepsis-related deaths in the U.S. alone. The cornerstones of optimal sepsis care are early recognition accompanied by appropriate antimicrobial therapy, and use of evidence-based hemodynamic therapies such as fluid resuscitation and vasoactive medications. While data-driven approaches based on machine learning (ML) have shown promise in finding patterns in high-dimensional clinical data to forecast sepsis among hospitalized patients, there are no clinically validated and FDA-approved clinical decision support (CDS) system that can reliably identify patients at risk of developing sepsis. Moreover, existing ML-based solutions are as good as the quality of the data presented to them, and the presence of outliers and missingness can have deleterious effects on their performance. For instance, it has been suggested that such systems are essentially looking over clinician's shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. As such, there is a critical need for sepsis prediction tools that can effectively use the routinely collected EHR data, assess prediction confidence, and if needed, take necessary steps to gather additional information to reduce prediction uncertainty and improve diagnostic accuracy without significant demand on the end-users. This project aims to assess the clinical utility, safety, and efficacy of a novel uncertainty-aware sepsis prediction system designed and developed in collaboration between UC San Diego Health and Healcisio Inc., a UCSD start-up focused on scalable development and commercialization of advanced analytical systems in critically care settings. The Healcisio system is explicitly designed to improve compliance with the Centers for Medicaid and Medicare Services (CMS) care protocol for sepsis (the SEP1 bundle) and to address the existing delays and variabilities in determining the sepsis onset time, so that life-saving antibiotics and hemodynamic support can be delivered in a timely fashion. To maintain software quality assurance a quality management system (QMS) will be developed to accompany a 510(k) FDA submission package to demonstrate safety and effectiveness. To enhance hospital quality improvement (QI) teams’ ability to measure impact of earlier recognition and SEP-1 bundle compliance, a novel quality measure (SEP1+) and a causal impact analysis tool is introduced. Ultimately, the novel technologies developed and tested under this project will enhance our ability to use advanced analytics to predict adverse events, assess patients’ response to therapy, and optimize and personalize care at the beside through a rapid-cycle ‘learning healthcare system’ framework.